Data mining is an iterative process filled with trial and error. The main steps of this process usually include [1]}: business understanding, feature engineering, pattern discovery, and evaluation. Out of all these stages, feature engineering is disputably the most difficult and time-consuming task because it is domain-specific. During feature design, data miners repeatedly gather data, integrate them, clean them and preprocess them, checking with each iteration whether the newly exposed attributes improve the learning model [2]}. As a result of intensive feature engineering, one occasionally faces a scenario where a learning model has difficulties with incorporating all the designed features. In such cases, a feature selection step is needed.
